With the recent success of service assistants such as Alexa, Cortana and Siri, research attempts to expanding spoken language understanding (SLU) applications are becoming ubiquitous [1]}. In canonical task-oriented dialogs, SLU establishes so-called semantic frames by capturing semantics in terms of intents and slots from speech recognized utterances [2]}. These intents specify goals which commit speakers to some course of actions, like requesting, informing or acknowledging with a series of semantic notions known as slots. In the full dialog scenario, we refer these intents for an utterance within a dialog turn as dialog acts [3]}.
In Table REF  as an example in Microsoft dialogue challenge dataset [4]}, each utterance or response may involve more than one dialog acts with or without specific targeted slots.
